添加了SVM无核函数的，注释参考有有核函数的-纯无核函数测试

Author: jiangzhonglian

src/python/6.SVM/svm-complete.py

@@ -531,123 +531,3 @@ def testDigits(kTup=('rbf', 10)):
         predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
         if sign(predict) != sign(labelArr[i]): errorCount += 1
     print("the test error rate is: %f" % (float(errorCount) / m))
-
-
-'''#######********************************
-Non-Kernel VErsions below
-'''  #######********************************
-
-
-class optStructK:
-    def __init__(self, dataMatIn, classLabels, C, toler):  # Initialize the structure with the parameters
-        self.X = dataMatIn
-        self.labelMat = classLabels
-        self.C = C
-        self.tol = toler
-        self.m = shape(dataMatIn)[0]
-        self.alphas = mat(zeros((self.m, 1)))
-        self.b = 0
-        self.eCache = mat(zeros((self.m, 2)))  # first column is valid flag
-
-
-def calcEkK(oS, k):
-    fXk = float(multiply(oS.alphas, oS.labelMat).T * (oS.X * oS.X[k, :].T)) + oS.b
-    Ek = fXk - float(oS.labelMat[k])
-    return Ek
-
-
-def selectJK(i, oS, Ei):  # this is the second choice -heurstic, and calcs Ej
-    maxK = -1
-    maxDeltaE = 0
-    Ej = 0
-    oS.eCache[i] = [1, Ei]  # set valid #choose the alpha that gives the maximum delta E
-    validEcacheList = nonzero(oS.eCache[:, 0].A)[0]
-    if (len(validEcacheList)) > 1:
-        for k in validEcacheList:  # loop through valid Ecache values and find the one that maximizes delta E
-            if k == i: continue  # don't calc for i, waste of time
-            Ek = calcEk(oS, k)
-            deltaE = abs(Ei - Ek)
-            if (deltaE > maxDeltaE):
-                maxK = k
-                maxDeltaE = deltaE
-                Ej = Ek
-        return maxK, Ej
-    else:  # in this case (first time around) we don't have any valid eCache values
-        j = selectJrand(i, oS.m)
-        Ej = calcEk(oS, j)
-    return j, Ej
-
-
-def updateEkK(oS, k):  # after any alpha has changed update the new value in the cache
-    Ek = calcEk(oS, k)
-    oS.eCache[k] = [1, Ek]
-
-
-def innerLK(i, oS):
-    Ei = calcEk(oS, i)
-    if ((oS.labelMat[i] * Ei < -oS.tol) and (oS.alphas[i] < oS.C)) or (
-        (oS.labelMat[i] * Ei > oS.tol) and (oS.alphas[i] > 0)):
-        j, Ej = selectJ(i, oS, Ei)  # this has been changed from selectJrand
-        alphaIold = oS.alphas[i].copy()
-        alphaJold = oS.alphas[j].copy()
-        if (oS.labelMat[i] != oS.labelMat[j]):
-            L = max(0, oS.alphas[j] - oS.alphas[i])
-            H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
-        else:
-            L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
-            H = min(oS.C, oS.alphas[j] + oS.alphas[i])
-        if L == H:
-            print("L==H")
-            return 0
-        eta = 2.0 * oS.X[i, :] * oS.X[j, :].T - oS.X[i, :] * oS.X[i, :].T - oS.X[j, :] * oS.X[j, :].T
-        if eta >= 0:
-            print("eta>=0")
-            return 0
-        oS.alphas[j] -= oS.labelMat[j] * (Ei - Ej) / eta
-        oS.alphas[j] = clipAlpha(oS.alphas[j], H, L)
-        updateEk(oS, j)  # added this for the Ecache
-        if (abs(oS.alphas[j] - alphaJold) < 0.00001):
-            print("j not moving enough")
-            return 0
-        oS.alphas[i] += oS.labelMat[j] * oS.labelMat[i] * (alphaJold - oS.alphas[j])  # update i by the same amount as j
-        updateEk(oS, i)  # added this for the Ecache                    #the update is in the oppostie direction
-        b1 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[i, :].T - oS.labelMat[j] * (
-        oS.alphas[j] - alphaJold) * oS.X[i, :] * oS.X[j, :].T
-        b2 = oS.b - Ej - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[j, :].T - oS.labelMat[j] * (
-        oS.alphas[j] - alphaJold) * oS.X[j, :] * oS.X[j, :].T
-        if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]):
-            oS.b = b1
-        elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]):
-            oS.b = b2
-        else:
-            oS.b = (b1 + b2) / 2.0
-        return 1
-    else:
-        return 0
-
-
-def smoPK(dataMatIn, classLabels, C, toler, maxIter):  # full Platt SMO
-    oS = optStruct(mat(dataMatIn), mat(classLabels).transpose(), C, toler)
-    iter = 0
-    entireSet = True
-    alphaPairsChanged = 0
-    while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
-        alphaPairsChanged = 0
-        if entireSet:  # go over all
-            for i in range(oS.m):
-                alphaPairsChanged += innerL(i, oS)
-                print("fullSet, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
-            iter += 1
-        else:  # go over non-bound (railed) alphas
-            nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
-            for i in nonBoundIs:
-                alphaPairsChanged += innerL(i, oS)
-                print("non-bound, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
-            iter += 1
-        if entireSet:
-            entireSet = False  # toggle entire set loop
-        elif (alphaPairsChanged == 0):
-            entireSet = True
-        print("iteration number: %d" % iter)
-    return oS.b, oS.alphas
-

src/python/6.SVM/svm-complete_Non-Kernel.py

@@ -257,119 +257,3 @@ def plotfig_SVM(xArr, yArr, ws, b, alphas):
     # 画图
     ws = calcWs(alphas, dataArr, labelArr)
     plotfig_SVM(dataArr, labelArr, ws, b, alphas)
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-def testRbf(k1=1.3):
-    dataArr, labelArr = loadDataSet('testSetRBF.txt')
-    b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1))  # C=200 important
-    datMat = mat(dataArr)
-    labelMat = mat(labelArr).transpose()
-    svInd = nonzero(alphas.A > 0)[0]
-    sVs = datMat[svInd]  # get matrix of only support vectors
-    labelSV = labelMat[svInd]
-    print("there are %d Support Vectors" % shape(sVs)[0])
-    m, n = shape(datMat)
-    errorCount = 0
-    for i in range(m):
-        kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
-        predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
-        if sign(predict) != sign(labelArr[i]): errorCount += 1
-    print("the training error rate is: %f" % (float(errorCount) / m))
-    dataArr, labelArr = loadDataSet('testSetRBF2.txt')
-    errorCount = 0
-    datMat = mat(dataArr)
-    labelMat = mat(labelArr).transpose()
-    m, n = shape(datMat)
-    for i in range(m):
-        kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
-        predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
-        if sign(predict) != sign(labelArr[i]): errorCount += 1
-    print("the test error rate is: %f" % (float(errorCount) / m))
-
-
-def img2vector(filename):
-    returnVect = zeros((1, 1024))
-    fr = open(filename)
-    for i in range(32):
-        lineStr = fr.readline()
-        for j in range(32):
-            returnVect[0, 32 * i + j] = int(lineStr[j])
-    return returnVect
-
-
-def loadImages(dirName):
-    from os import listdir
-    hwLabels = []
-    print(dirName)
-    trainingFileList = listdir(dirName)  # load the training set
-    m = len(trainingFileList)
-    trainingMat = zeros((m, 1024))
-    for i in range(m):
-        fileNameStr = trainingFileList[i]
-        fileStr = fileNameStr.split('.')[0]  # take off .txt
-        classNumStr = int(fileStr.split('_')[0])
-        if classNumStr == 9:
-            hwLabels.append(-1)
-        else:
-            hwLabels.append(1)
-        trainingMat[i, :] = img2vector('%s/%s' % (dirName, fileNameStr))
-    return trainingMat, hwLabels
-
-
-def testDigits(kTup=('rbf', 10)):
-    dataArr, labelArr = loadImages('trainingDigits')
-    b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, kTup)
-    datMat = mat(dataArr)
-    labelMat = mat(labelArr).transpose()
-    svInd = nonzero(alphas.A > 0)[0]
-    sVs = datMat[svInd]
-    labelSV = labelMat[svInd]
-    print("there are %d Support Vectors" % shape(sVs)[0])
-    m, n = shape(datMat)
-    errorCount = 0
-    for i in range(m):
-        kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
-        predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
-        if sign(predict) != sign(labelArr[i]): errorCount += 1
-    print("the training error rate is: %f" % (float(errorCount) / m))
-    dataArr, labelArr = loadImages('testDigits')
-    errorCount = 0
-    datMat = mat(dataArr)
-    labelMat = mat(labelArr).transpose()
-    m, n = shape(datMat)
-    for i in range(m):
-        kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
-        predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
-        if sign(predict) != sign(labelArr[i]): errorCount += 1
-    print("the test error rate is: %f" % (float(errorCount) / m))
-